{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### joblib保存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 43.8 s, sys: 8.37 s, total: 52.2 s\n",
      "Wall time: 57.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# 导入训练数据集\n",
    "data_date = pd.read_csv('../../preprocess_data/train_x_date.csv').drop(columns=['id','loan_hour'])\n",
    "data_raw = joblib.load('../../preprocess_data_new/train_ax_nodup.lz4').head(33465).drop(columns=['loan_dt','id','tag'])\n",
    "data_tag = pd.read_csv('../../preprocess_data/train_x_33465.csv',usecols=['tag'])\n",
    "major_df = joblib.load('../../preprocess_data_discrete/major_df.lz4').head(33465)\n",
    "data_null = pd.read_csv('../../preprocess_data_new/train_ax_row_null.csv',nrows=33465).drop(columns=['id'])\n",
    "maj_cnt_df = joblib.load('../../preprocess_data_discrete/maj_cnt_df.lz4').head(33465)\n",
    "\n",
    "data = pd.concat([data_date,data_raw,data_tag,major_df,data_null,maj_cnt_df],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.78 s, sys: 1.86 s, total: 10.6 s\n",
      "Wall time: 18.7 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['./train_data1.lz4']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "joblib.dump(data,'./train_data1.lz4',compress='lz4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.92 s, sys: 1.36 s, total: 9.28 s\n",
      "Wall time: 10.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# 导入测试数据集\n",
    "\n",
    "valid_date = pd.read_csv('../../preprocess_data/valid_date.csv').drop(columns=['id','loan_hour'])\n",
    "valid_raw = joblib.load('../../preprocess_data_new/valid_nodup.lz4').drop(columns=['id','loan_dt'])\n",
    "valid_tag = pd.read_csv('../predict_tag/tag.csv',usecols=['tag'])\n",
    "major_test = joblib.load('../../preprocess_data_discrete/major_test.lz4')\n",
    "valid_null = pd.read_csv('../../preprocess_data_new/valid_row_null.csv').drop(columns=['id'])\n",
    "maj_cnt_test = joblib.load('../../preprocess_data_discrete/maj_cnt_test.lz4')\n",
    "\n",
    "valid = pd.concat([valid_date,valid_raw,valid_tag,major_test,valid_null,maj_cnt_test],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.46 s, sys: 640 ms, total: 6.1 s\n",
      "Wall time: 6.14 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['./valid_data1.lz4']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "joblib.dump(valid,'./valid_data1.lz4',compress='lz4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.28 s, sys: 2.88 s, total: 10.2 s\n",
      "Wall time: 12.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# train\n",
    "data = joblib.load('./train_data1.lz4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_label = pd.read_csv('../../preprocess_data/train_y_33465.csv',usecols=['label'])\n",
    "x = data.fillna(-1).values\n",
    "y = data_label.values.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.5 s, sys: 6.99 s, total: 13.5 s\n",
      "Wall time: 14.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# test\n",
    "valid = joblib.load('./valid_data1.lz4')\n",
    "x_test = valid.fillna(-1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_id = pd.read_csv('../../preprocess_data_new/valid_date.csv',usecols=['id']).values.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 21.5 s, sys: 3.28 s, total: 24.8 s\n",
      "Wall time: 24.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# 导入训练数据集\n",
    "data_date = pd.read_csv('../../preprocess_data/train_x_date.csv').drop(columns=['id','loan_hour'])\n",
    "week_df = joblib.load('../../preprocess_data_discrete/week_df.lz4').head(33465)\n",
    "data_raw = joblib.load('../../preprocess_data_new/train_ax_nodup.lz4').head(33465).drop(columns=['loan_dt','id','tag'])\n",
    "data_null = pd.read_csv('../../preprocess_data_new/train_ax_row_null.csv',nrows=33465).drop(columns=['id'])\n",
    "data_tag = pd.read_csv('../../preprocess_data/train_x_33465.csv',usecols=['tag'])\n",
    "\n",
    "# maj_cnt_df = joblib.load('../../preprocess_data_discrete/maj_cnt_df.lz4').head(33465)\n",
    "# cat_df = joblib.load('../../preprocess_data_discrete/cat_df.lz4').head(33465)\n",
    "# discrete_df = joblib.load('../../preprocess_data_discrete/discrete_df.lz4').head(33465)\n",
    "# scale_df = joblib.load('../../preprocess_data_discrete/scale_df.lz4').head(33465)\n",
    "# major_df = joblib.load('../../preprocess_data_discrete/major_df.lz4').head(33465)\n",
    "# day_val_df = joblib.load('../../preprocess_data_discrete/day_val_df.lz4').head(33465)\n",
    "\n",
    "data_label = pd.read_csv('../../preprocess_data/train_y_33465.csv',usecols=['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tag    0\n",
       "Name: 30464, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_tag.iloc[30464]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([data_date,data_raw,data_null,data_tag],axis=1,copy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33465, 6702)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = data.fillna(-1).values\n",
    "y = data_label.values.ravel()\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.8 s, sys: 1.66 s, total: 10.5 s\n",
      "Wall time: 12.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# 导入测试数据集\n",
    "\n",
    "valid_date = pd.read_csv('../../preprocess_data/valid_date.csv').drop(columns=['id','loan_hour'])\n",
    "valid_raw = joblib.load('../../preprocess_data_new/valid_nodup.lz4').drop(columns=['id','loan_dt'])\n",
    "valid_tag = pd.read_csv('../predict_tag/tag.csv',usecols=['tag'])\n",
    "valid_null = pd.read_csv('../../preprocess_data_new/valid_row_null.csv').drop(columns=['id'])\n",
    "major_test = joblib.load('../../preprocess_data_discrete/major_test.lz4')\n",
    "maj_cnt_test = joblib.load('../../preprocess_data_discrete/maj_cnt_test.lz4')\n",
    "\n",
    "valid = pd.concat([valid_date,valid_raw,valid_tag,valid_null,major_test,maj_cnt_test],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = valid.fillna(-1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000, 11334)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据线上和线下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., ..., 0., 0., 0.])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.08 s, sys: 1.03 s, total: 3.1 s\n",
      "Wall time: 3.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# 本地验证\n",
    "# 1.有标签\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,random_state=2018,test_size=0.3,stratify=y)\n",
    "# 2. 过采样\n",
    "# from imblearn.over_sampling import BorderlineSMOTE\n",
    "# x_resampled, y_resampled = BorderlineSMOTE().fit_resample(x_train, y_train)\n",
    "\n",
    "# 线上测试\n",
    "# x_train = x\n",
    "# y_train = y\n",
    "\n",
    "# valid_date = pd.read_csv('../../preprocess_data/valid_date.csv').drop(columns=['id','loan_hour'])\n",
    "# # valid_raw = pd.read_csv('../../preprocess_data/valid.csv').drop(columns=['id','loan_dt'])\n",
    "# valid_raw = pd.read_csv('../../preprocess_data_new/valid_nodup.csv').drop(columns=['id','loan_dt'])\n",
    "# valid_null = pd.read_csv('../../preprocess_data_new/valid_row_null.csv').drop(columns=['id'])\n",
    "# valid_tag = pd.read_csv('../predict_tag/tag.csv',usecols=['tag'])\n",
    "# valid = pd.concat([valid_date,valid_raw,valid_null,valid_tag],axis=1)\n",
    "# test_id = pd.read_csv('../../preprocess_data_new/valid_date.csv',usecols=['id']).values.ravel()\n",
    "# x_test = valid.fillna(-1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23425, 6702)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 本地"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始训练！\n",
      "[0]\tvalid-auc:0.721024\n",
      "[1]\tvalid-auc:0.743645\n",
      "[2]\tvalid-auc:0.757624\n",
      "[3]\tvalid-auc:0.76535\n",
      "[4]\tvalid-auc:0.76947\n",
      "[5]\tvalid-auc:0.771044\n",
      "[6]\tvalid-auc:0.772436\n",
      "[7]\tvalid-auc:0.774228\n",
      "[8]\tvalid-auc:0.778648\n",
      "[9]\tvalid-auc:0.780205\n",
      "[10]\tvalid-auc:0.782302\n",
      "[11]\tvalid-auc:0.783317\n",
      "[12]\tvalid-auc:0.783445\n",
      "[13]\tvalid-auc:0.785957\n",
      "[14]\tvalid-auc:0.786131\n",
      "[15]\tvalid-auc:0.786251\n",
      "[16]\tvalid-auc:0.787698\n",
      "[17]\tvalid-auc:0.788291\n",
      "[18]\tvalid-auc:0.788792\n",
      "[19]\tvalid-auc:0.790014\n",
      "[20]\tvalid-auc:0.790437\n",
      "[21]\tvalid-auc:0.79025\n",
      "[22]\tvalid-auc:0.790258\n",
      "[23]\tvalid-auc:0.790956\n",
      "[24]\tvalid-auc:0.791717\n",
      "[25]\tvalid-auc:0.792507\n",
      "[26]\tvalid-auc:0.793169\n",
      "[27]\tvalid-auc:0.793711\n",
      "[28]\tvalid-auc:0.794321\n",
      "[29]\tvalid-auc:0.795006\n",
      "[30]\tvalid-auc:0.795449\n",
      "[31]\tvalid-auc:0.795942\n",
      "[32]\tvalid-auc:0.796105\n",
      "[33]\tvalid-auc:0.796183\n",
      "[34]\tvalid-auc:0.797065\n",
      "[35]\tvalid-auc:0.797853\n",
      "[36]\tvalid-auc:0.798445\n",
      "[37]\tvalid-auc:0.798718\n",
      "[38]\tvalid-auc:0.799216\n",
      "[39]\tvalid-auc:0.800073\n",
      "[40]\tvalid-auc:0.800651\n",
      "[41]\tvalid-auc:0.801379\n",
      "[42]\tvalid-auc:0.801128\n",
      "[43]\tvalid-auc:0.801307\n",
      "[44]\tvalid-auc:0.801705\n",
      "[45]\tvalid-auc:0.802712\n",
      "[46]\tvalid-auc:0.802843\n",
      "[47]\tvalid-auc:0.803117\n",
      "[48]\tvalid-auc:0.803223\n",
      "[49]\tvalid-auc:0.803532\n",
      "[50]\tvalid-auc:0.80387\n",
      "[51]\tvalid-auc:0.80368\n",
      "[52]\tvalid-auc:0.804392\n",
      "[53]\tvalid-auc:0.804724\n",
      "[54]\tvalid-auc:0.805158\n",
      "[55]\tvalid-auc:0.805708\n",
      "[56]\tvalid-auc:0.80624\n",
      "[57]\tvalid-auc:0.806508\n",
      "[58]\tvalid-auc:0.806576\n",
      "[59]\tvalid-auc:0.806324\n",
      "[60]\tvalid-auc:0.806467\n",
      "[61]\tvalid-auc:0.806553\n",
      "[62]\tvalid-auc:0.8071\n",
      "[63]\tvalid-auc:0.807386\n",
      "[64]\tvalid-auc:0.807957\n",
      "[65]\tvalid-auc:0.807885\n",
      "[66]\tvalid-auc:0.808\n",
      "[67]\tvalid-auc:0.808439\n",
      "[68]\tvalid-auc:0.809084\n",
      "[69]\tvalid-auc:0.80929\n",
      "[70]\tvalid-auc:0.809534\n",
      "[71]\tvalid-auc:0.809879\n",
      "[72]\tvalid-auc:0.810314\n",
      "[73]\tvalid-auc:0.810492\n",
      "[74]\tvalid-auc:0.810703\n",
      "[75]\tvalid-auc:0.810831\n",
      "[76]\tvalid-auc:0.811084\n",
      "[77]\tvalid-auc:0.811543\n",
      "[78]\tvalid-auc:0.811485\n",
      "[79]\tvalid-auc:0.811088\n",
      "[80]\tvalid-auc:0.811333\n",
      "[81]\tvalid-auc:0.811531\n",
      "[82]\tvalid-auc:0.812082\n",
      "[83]\tvalid-auc:0.812109\n",
      "[84]\tvalid-auc:0.812271\n",
      "[85]\tvalid-auc:0.812515\n",
      "[86]\tvalid-auc:0.812697\n",
      "[87]\tvalid-auc:0.812624\n",
      "[88]\tvalid-auc:0.813118\n",
      "[89]\tvalid-auc:0.813308\n",
      "[90]\tvalid-auc:0.813591\n",
      "[91]\tvalid-auc:0.814039\n",
      "[92]\tvalid-auc:0.814172\n",
      "[93]\tvalid-auc:0.814479\n",
      "[94]\tvalid-auc:0.814437\n",
      "[95]\tvalid-auc:0.814959\n",
      "[96]\tvalid-auc:0.81483\n",
      "[97]\tvalid-auc:0.814788\n",
      "[98]\tvalid-auc:0.814762\n",
      "[99]\tvalid-auc:0.81471\n",
      "[100]\tvalid-auc:0.815136\n",
      "[101]\tvalid-auc:0.815393\n",
      "[102]\tvalid-auc:0.815438\n",
      "[103]\tvalid-auc:0.81558\n",
      "[104]\tvalid-auc:0.815602\n",
      "[105]\tvalid-auc:0.815765\n",
      "[106]\tvalid-auc:0.815914\n",
      "[107]\tvalid-auc:0.816239\n",
      "[108]\tvalid-auc:0.816396\n",
      "[109]\tvalid-auc:0.816653\n",
      "[110]\tvalid-auc:0.816874\n",
      "[111]\tvalid-auc:0.817059\n",
      "[112]\tvalid-auc:0.81728\n",
      "[113]\tvalid-auc:0.817345\n",
      "[114]\tvalid-auc:0.817346\n",
      "[115]\tvalid-auc:0.817374\n",
      "[116]\tvalid-auc:0.817498\n",
      "[117]\tvalid-auc:0.817504\n",
      "[118]\tvalid-auc:0.81755\n",
      "[119]\tvalid-auc:0.818073\n",
      "[120]\tvalid-auc:0.818015\n",
      "[121]\tvalid-auc:0.818189\n",
      "[122]\tvalid-auc:0.818015\n",
      "[123]\tvalid-auc:0.818604\n",
      "[124]\tvalid-auc:0.818733\n",
      "[125]\tvalid-auc:0.818654\n",
      "[126]\tvalid-auc:0.818994\n",
      "[127]\tvalid-auc:0.81888\n",
      "[128]\tvalid-auc:0.819192\n",
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      "[130]\tvalid-auc:0.819335\n",
      "[131]\tvalid-auc:0.819287\n",
      "[132]\tvalid-auc:0.819324\n",
      "[133]\tvalid-auc:0.819168\n",
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      "[137]\tvalid-auc:0.819409\n",
      "[138]\tvalid-auc:0.81954\n",
      "[139]\tvalid-auc:0.81951\n",
      "[140]\tvalid-auc:0.819426\n",
      "[141]\tvalid-auc:0.819476\n",
      "[142]\tvalid-auc:0.819495\n",
      "[143]\tvalid-auc:0.819374\n",
      "[144]\tvalid-auc:0.819425\n",
      "[145]\tvalid-auc:0.819686\n",
      "[146]\tvalid-auc:0.819686\n",
      "[147]\tvalid-auc:0.819695\n",
      "[148]\tvalid-auc:0.81979\n",
      "[149]\tvalid-auc:0.819758\n",
      "[150]\tvalid-auc:0.819608\n",
      "[151]\tvalid-auc:0.81959\n",
      "[152]\tvalid-auc:0.819573\n",
      "[153]\tvalid-auc:0.819644\n",
      "[154]\tvalid-auc:0.819758\n",
      "[155]\tvalid-auc:0.819894\n",
      "[156]\tvalid-auc:0.819871\n",
      "[157]\tvalid-auc:0.819932\n",
      "[158]\tvalid-auc:0.820172\n",
      "[159]\tvalid-auc:0.820345\n",
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      "[171]\tvalid-auc:0.819735\n",
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      "[187]\tvalid-auc:0.820435\n",
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      "[192]\tvalid-auc:0.820609\n",
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      "[252]\tvalid-auc:0.820842\n",
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      "[264]\tvalid-auc:0.820472\n",
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      "[272]\tvalid-auc:0.820722\n",
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      "[285]\tvalid-auc:0.821068\n",
      "[286]\tvalid-auc:0.820963\n",
      "[287]\tvalid-auc:0.820982\n",
      "[288]\tvalid-auc:0.821133\n",
      "[289]\tvalid-auc:0.821034\n",
      "[290]\tvalid-auc:0.820984\n",
      "[291]\tvalid-auc:0.820987\n",
      "[292]\tvalid-auc:0.820858\n",
      "[293]\tvalid-auc:0.820886\n",
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      "[297]\tvalid-auc:0.821405\n",
      "[298]\tvalid-auc:0.821388\n",
      "[299]\tvalid-auc:0.821412\n",
      "CPU times: user 54min 48s, sys: 19.4 s, total: 55min 7s\n",
      "Wall time: 1min 56s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#2.建立模型 \n",
    "model_names = ['rfc','gbc','xgb','lgb']\n",
    "model_name = model_names[2]\n",
    "if model_name == 'rfc':\n",
    "    # RandomForestClassifier\n",
    "    from sklearn.ensemble import RandomForestClassifier\n",
    "    rfc  =RandomForestClassifier(n_estimators=2500,n_jobs =36,max_features='sqrt',class_weight='balanced',verbose =1,random_state=2018)\n",
    "elif model_name == 'gbc':\n",
    "    from sklearn.ensemble import GradientBoostingClassifier\n",
    "    gbc = GradientBoostingClassifier(loss='deviance',learning_rate =0.1,n_estimators=300,subsample=0.9,max_depth=3,verbose=1,random_state=2018)\n",
    "elif model_name == 'xgb':\n",
    "    import xgboost as xgb\n",
    "    params={\n",
    "    'booster':'gbtree',\n",
    "    'objective': 'binary:logistic',\n",
    "    'early_stopping_rounds':100,\n",
    "    'scale_pos_weight': float(len(data_label)-np.sum(data_label.values))/float(np.sum(data_label.values)),  # 负例样本除以正例样本\n",
    "    'eval_metric': 'auc',\n",
    "    'gamma':1,\n",
    "    'max_depth':6,\n",
    "    'lambda':1,\n",
    "    'subsample':0.9,\n",
    "    'colsample_bytree':0.9,\n",
    "    'min_child_weight':1, \n",
    "    'eta': 0.04,\n",
    "    'seed':2010,\n",
    "    'nthread':32\n",
    "        }\n",
    "elif model_name == 'lgb':\n",
    "    import lightgbm as lgb\n",
    "    parameters = {\n",
    "        'boost':'gbdt',\n",
    "        'num_leaves':135, \n",
    "        'scale_pos_weight':float(len(y_train)-np.sum(y_train.ravel()))/float(np.sum(y_train.ravel())),\n",
    "        'max_depth':-1,\n",
    "        'learning_rate':.05,\n",
    "        'max_bin':200,\n",
    "        'min_data_in_leaf' : 60,\n",
    "        'objective':'binary',\n",
    "        'metric':'auc',\n",
    "        'verbose':1,\n",
    "        'num_threads':36\n",
    "    }\n",
    "       \n",
    "else:\n",
    "    pass\n",
    "\n",
    "#3.训练模型\n",
    "print('开始训练！')\n",
    "import joblib\n",
    "if model_name == 'rfc':\n",
    "    rfc.fit(x_train,y_train)\n",
    "#     joblib.dump(rfc,'./model/rfc_model')\n",
    "elif model_name == 'gbc':\n",
    "    gbc.fit(x_train,y_train)\n",
    "#     joblib.dump(gbc,'./model/rgbc_model')\n",
    "elif model_name =='xgb':\n",
    "    dtrain = xgb.DMatrix(x_train,y_train)\n",
    "    dvalid = xgb.DMatrix(x_test,y_test)\n",
    "    dtest = xgb.DMatrix(x_test)\n",
    "    watchlist  = [(dvalid,'valid')]\n",
    "    xgb_model = xgb.train(params,dtrain,num_boost_round=300,evals=watchlist)\n",
    "#     xgb_model.save_model('./model/xgb_model')\n",
    "elif model_name == 'lgb':\n",
    "    lgb_train = lgb.Dataset(x_train, y_train.ravel())\n",
    "    lgb_model = lgb.train(parameters,lgb_train,num_boost_round=300)\n",
    "#     lgb_model.save_model('./model/lgb_model')\n",
    "else:\n",
    "    pass\n",
    "# lg.fit(x_train,y_train.ravel())\n",
    "# lsvm.fit(x_train,y_train)\n",
    "# history = model.fit(x_train,y_train,epochs=2,batch_size=1024,class_weight = 'auto',validation_data=(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始预测！\n",
      "AUC: 0.8307681067058688\n"
     ]
    }
   ],
   "source": [
    "#4.预测结果\n",
    "print('开始预测！')\n",
    "if model_name=='rfc':\n",
    "    y_pre = rfc.predict_proba(x_test)[:,1]\n",
    "elif model_name=='gbc':\n",
    "    y_pre = gbc.predict_proba(x_test)[:,1]\n",
    "elif model_name=='xgb':\n",
    "    dtest = xgb.DMatrix(x_test)\n",
    "    y_pre = xgb_model.predict(dtest).ravel()\n",
    "elif model_name=='lgb':\n",
    "    y_pre = lgb_model.predict(x_test)\n",
    "else:\n",
    "    pass\n",
    "\n",
    "# y_pre = lsvm.decision_function(X_test)\n",
    "# y_pre = lg.predict(X_test)\n",
    "from sklearn import metrics\n",
    "auc = metrics.roc_auc_score(y_test, y_pre)\n",
    "print('AUC:',auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 未作stratify\n",
    "nodup + null + tag +(fillna(-1))AUC: 0.82785  \n",
    "nodup + null + tag +(fillna(-1)) + maj_cnt AUC:    \n",
    "nodup + null + tag +(fillna(-1)) + major_df + maj_cnt_df  AUC: 0.832029(200)\n",
    "nodup + null + tag +(fillna(-1)) + major_df AUC:0.829731  \n",
    "nodup + null + tag +(fillna(-1)) + major_df + maj_cnt_df + discrete_df:0.8301\n",
    "data_raw被替换 AUC：0.821537  \n",
    "nodup + null + tag +(fillna(-1)) + major_df + maj_cnt_df + week_df AUC: 0.826666  \n",
    "nodup + null + tag +(fillna(-1))  + maj_cnt_df + week_df AUC:0.828063  \n",
    "nodup + null + tag +(fillna(-1))  + maj_cnt_df + maj_cnt_df + day_val_df AUC: 0.828095"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 线上"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### xgb bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_id = pd.read_csv('../../preprocess_data_new/valid_date.csv',usecols=['id']).values.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "dtrain = xgb.DMatrix(x, y, feature_names=list(data.columns))\n",
    "dtest = xgb.DMatrix(x_test, feature_names=list(valid.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pipeline(dtrain,dtest,test_id,iteration,random_seed,gamma,max_depth,lambd,subsample,colsample_bytree,min_child_weight):\n",
    "    if max_depth==6:\n",
    "        num_boost_round = 210\n",
    "    elif max_depth==7:\n",
    "        num_boost_round = 180\n",
    "    elif max_depth==8:\n",
    "        num_boost_round = 150\n",
    "    \n",
    "    params={\n",
    "    'booster':'gbtree',\n",
    "    'objective': 'binary:logistic',\n",
    "    'early_stopping_rounds':100,\n",
    "    'scale_pos_weight': float(len(data_label)-np.sum(data_label.values))/float(np.sum(data_label.values)),  # 负例样本除以正例样本\n",
    "    'eval_metric': 'auc',\n",
    "    'gamma':gamma,\n",
    "    'max_depth':max_depth,\n",
    "    'lambda':lambd,\n",
    "    'subsample':subsample,\n",
    "    'colsample_bytree':colsample_bytree,\n",
    "    'min_child_weight':min_child_weight, \n",
    "    'eta': 0.04,\n",
    "    'seed':random_seed,\n",
    "    'nthread':32\n",
    "        }\n",
    "    watchlist  = [(dtrain,'train')]\n",
    "    model = xgb.train(params,dtrain,num_boost_round=num_boost_round,evals=watchlist)\n",
    "    model.save_model('./xgb_models1/xgb{0}.model'.format(iteration))\n",
    "    \n",
    "    #predict test set\n",
    "    test_y = model.predict(dtest)\n",
    "    test_result = pd.DataFrame(test_id,columns=[\"id\"])\n",
    "    test_result['score'] = test_y\n",
    "    test_result.to_csv(\"./xgb_preds1/xgb{0}.csv\".format(iteration),index=None,encoding='utf-8')\n",
    "    \n",
    "    #get feature score\n",
    "    feature_score = model.get_fscore()\n",
    "    feature_score = sorted(feature_score.items(), key=lambda x:x[1],reverse=True)\n",
    "    fs = []\n",
    "    for (key,value) in feature_score:\n",
    "        fs.append(\"{0},{1}\\n\".format(key,value))\n",
    "    \n",
    "    with open('./xgb_featurescores1/feature_score_{0}.csv'.format(iteration),'w') as f:\n",
    "        f.writelines(\"feature,score\\n\")\n",
    "        f.writelines(fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-auc:0.794805\n",
      "[1]\ttrain-auc:0.822588\n",
      "[2]\ttrain-auc:0.841422\n",
      "[3]\ttrain-auc:0.850825\n",
      "[4]\ttrain-auc:0.855166\n",
      "[5]\ttrain-auc:0.859018\n",
      "[6]\ttrain-auc:0.861039\n",
      "[7]\ttrain-auc:0.863852\n",
      "[8]\ttrain-auc:0.86654\n",
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      "[10]\ttrain-auc:0.86986\n",
      "[11]\ttrain-auc:0.872057\n",
      "[12]\ttrain-auc:0.874137\n",
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      "[209]\ttrain-auc:0.957919\n",
      "CPU times: user 20h 16s, sys: 7min 22s, total: 20h 7min 38s\n",
      "Wall time: 42min 59s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import sys,random\n",
    "\n",
    "random_seed = list(range(1000,2000,20))\n",
    "gamma = [i/1000.0 for i in range(100,200,2)]\n",
    "max_depth = [6,7,8]\n",
    "lambd = list(range(200,400,2))\n",
    "subsample = [i/1000.0 for i in range(600,700,2)]\n",
    "colsample_bytree = [i/1000.0 for i in range(250,350,2)]\n",
    "min_child_weight = [i/1000.0 for i in range(200,300,2)]\n",
    "random.shuffle(random_seed)\n",
    "random.shuffle(gamma)\n",
    "random.shuffle(max_depth)\n",
    "random.shuffle(lambd)\n",
    "random.shuffle(subsample)\n",
    "random.shuffle(colsample_bytree)\n",
    "random.shuffle(min_child_weight)\n",
    "\n",
    "with open('params.pkl','wb') as f:\n",
    "    joblib.dump((random_seed,gamma,max_depth,lambd,subsample,colsample_bytree,min_child_weight),f)\n",
    "\n",
    "#to reproduce my result, uncomment following lines\n",
    "\"\"\"\n",
    "with open('params_for_reproducing.pkl','rb') as f:\n",
    "    random_seed,gamma,max_depth,lambd,subsample,colsample_bytree,min_child_weight = pickle.load(f)    \n",
    "\"\"\"\n",
    "\n",
    "for i in range(36):\n",
    "    pipeline(dtrain,dtest,test_id,i,random_seed[i],gamma[i],max_depth[i%3],lambd[i],subsample[i],colsample_bytree[i],min_child_weight[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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